Blood vessel analysis apparatus, medical image diagnosis apparatus, and blood vessel analysis method

ABSTRACT

According to one embodiment, a structuring circuitry temporarily structures a dynamical model of analysis processing based on the time-series medical image. The identification circuitry identifies a latent variable of the dynamical model so that at least one of a prediction value of a blood vessel morphology and a prediction value of a bloodstream based on the temporarily structured dynamical model is in conformity with at least one of an observation value of the blood vessel morphology and an observation value of the bloodstream measured in advance. The analysis circuitry analyzes the dynamical model to which the identified latent variable is allocated.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a Continuation application of PCT Application No.PCT/JP2013/082872, filed Dec. 6, 2013 and based upon and claims thebenefit of priority from the Japanese Patent Application No.2012-268714, filed Dec. 7, 2012, the entire contents of all of which areincorporated herein by reference.

FIELD

Embodiment described herein relate generally to a blood vessel analysisapparatus, a medical image diagnosis apparatus, and a blood vesselanalysis method.

BACKGROUND

It is desired to develop a noninvasive or minimally invasive techniquefor preventing and diagnosing stenosis of a coronary artery causingheart disease which is one of three major diseases, cerebral aneurysm,or stenosis caused by a plaque of a carotid artery which may be apremonition thereof.

Stenosis of a coronary artery is a serious pathologic change that maylead to ischemic heart disease. A diagnosis of stenosis of a coronaryartery is mainly Coronary Angiography (CAG) using a catheter. Adiagnosis index of an organic pathologic change of a coronary arteryincludes Fractional Flow Reserve (FFR). The FFR is defined as a ratio ofthe maximum coronary blood flow where stenosis exists with respect tothe maximum coronary blood flow where stenosis does not exist. The FFRis substantially the same as the ratio of a stenosis distal portioncoronary internal pressure with respect to a stenosis proximal portioncoronary internal pressure. The FFR is measured by a pressure sensorprovided at a catheter distal end. More specifically, a catheteroperation is required to measure the FFR.

When the analysis of stenosis of the coronary artery can be performedwith a heart CT, this is minimally invasive, and can reduce the burdenimposed on the patient and save the medical cost as compared with themeasurement of the FFR with the catheter operation. However, in theheart CT, only the index based on the size of a plaque region or athrombus region included in a CT image can be measured in a minimallyinvasive manner. If a pressure difference and the like before and afterthe thrombus can be measured based on the CT image by structural fluidanalysis, the effect exerted by the thrombus (or plaque) is expected tobe quantified.

Medical imaging techniques such as ultra-fast CT, cine angiography, MRI(magnetic resonance image method), ultrasonic imaging method, SPECT(single photon emission tomography), PET (positron emission tomography),and the like have been developed in terms of clinical aspect as adynamic evaluation of coronary circulation, and are used for evaluationof diagnosis and treatment methods.

However, it is difficult for a medical image diagnosis apparatus toaccurately recognize coronary microvessels. Even if a blood vessel shapeis clear, a medical image may include noises, and the threshold valuesetting at the boundary of a living tissue may be ambiguous. Asdescribed above, a blood vessel shape obtained by the medical imagediagnosis apparatus involves uncertainty.

When a medical image diagnosis apparatus is utilized in a clinicalapplication, analysis is often performed on the target of only a thickregion of coronary artery from the origin of the aorta at the upstreamof the coronary microvessels. Since the bloodstream of the coronaryartery is also greatly affected by the tonus of the coronarymicrovessels, it is the problem to appropriately set boundary conditionsof fluid analysis such as the volume of flow or pressure at the exit ofthe coronary artery of the thick region or the rate of change thereof.The bloodstream of the coronary artery receives mechanical factors ofpulsation of the heart (overall movement caused by pulsation, and forceddisplacement or external force due to local expansion and contraction,twisting, and shearing deformation). With the fluid analysis alone, theeffect of the mechanical factors such as pulsation of the heart cannotbe taken into consideration, and therefore, the volume of flowdistribution of the bloodstream and the internal pressure distributioncannot be accurately measured. On the other hand, a structure-fluidinteraction analysis is also carried out on the heart and the bloodvessel system captured in an image in view of the effects of themechanical factors. However, even when the structure-fluid interactionanalysis is performed, it is often difficult to correctly set thematerial model of the blood vessel and the plaque and the boundarycondition at the entrance and the exit of the blood vessel in the fluidanalysis of the blood (including contrast agent). When there is amicrovessel that is not captured in the image, the effect of themicrovessel given to the bloodstream cannot be taken into consideration.For this reason, the analysis result of the structure-fluid interactionanalysis may not be reproducing actual bloodstream and blood vesseldeformation. In a case where the boundary condition, the load condition,and the material model are not appropriate, or in a case where the bloodvessel involves great movement, there may be a problem in theconvergence and the analysis stability. As described above, inconventional structural fluid analysis of blood vessels, it may berequired to have large analysis resources and it may take an analysistime, or the error of the analysis result may increase, and therefore,there may be a problem in the utilization in actual clinical scenes.

BRIEF DESCRIPTION OF THE DRAWING

FIG. 1 is a figure illustrating a schematic block configuration of amedical image diagnosis apparatus (X-ray computed tomography apparatus)according to the present embodiment.

FIG. 2 is a figure illustrating an example of a dynamical model of atarget region of a structural fluid analysis according to the presentembodiment.

FIG. 3 is a figure illustrating a typical flow of structural fluidanalysis processing performed under the control of a system controlcircuitry of FIG. 1.

FIG. 4 is a figure illustrating a block configuration of the imageprocessing circuitry of FIG. 1.

FIG. 5 is a figure schematically illustrating a cross sectionperpendicular to a center line of a blood vessel region included in a CTimage.

FIG. 6 is a figure illustrating a time change of an aspect of a bloodvessel center line used for image tracking processing performed withimage analysis/tracking processing of FIG. 4.

FIG. 7 is a figure for explaining image tracking processing according tothe image analysis/tracking processing of FIG. 4, and is a figureillustrating an example of tracking processing between a time t and atime t+Δt.

FIG. 8 is a figure illustrating a cross section perpendicular to acenter line of a shape model structured by a dynamical model structuringcircuitry of FIG. 4.

FIG. 9 is a figure illustrating an example of a CT value-material modeltable stored in a storage of FIG. 1.

FIG. 10 is a figure for explaining allocation of a forced displacementhistory to a shape model performed by the dynamical model structuringcircuitry of FIG. 4.

FIG. 11 is a figure for explaining posterior distribution calculationand identification of an average internal pressure of a load condition(an average pressure in a blood vessel) according to hierarchicalBayesian model and Markov chain Monte Carlo methods performed by thestatistical identification circuitry of FIG. 4.

FIG. 12 is a figure for explaining identification of a material modelparameter and a posterior distribution calculation about the materialmodel parameter based on hierarchical Bayesian model and Markov chainMonte Carlo method (an equivalent elastic modulus of a blood vesselwall) performed with the statistical identification circuitry of FIG. 4.

FIG. 13 is a figure illustrating an example of display of a spacedistribution of an internal pressure which is one of dynamical indexesaccording to the display device of FIG. 1.

FIG. 14A is a figure illustrating another example of allocation of aforced displacement history according to the dynamical model structuringcircuitry of FIG. 4.

FIG. 14B is a figure illustrating another example of allocation of aforced displacement history according to the dynamical model structuringcircuitry of FIG. 4.

FIG. 15 is a figure illustrating another example of allocation of aforced displacement history according to the dynamical model structuringcircuitry of FIG. 4.

FIG. 16 is a figure illustrating another example of allocation of aforced displacement history according to the dynamical model structuringcircuitry of FIG. 4.

FIG. 17A is a schematic diagram of a heart where any collateral does notexist.

FIG. 17B is a schematic diagram of a heart where collateral exists.

FIG. 18 is a figure illustrating a typical flow of processing performedunder control of the system control circuitry according to anapplication example of the present embodiment.

FIG. 19 is a figure for explaining determination processing in which acollateral determination circuitry of an image processing circuitryaccording to an application example of the present embodiment determineswhether collateral exists or not.

DETAILED DESCRIPTION

In general, according to one embodiment, a blood vessel analysisapparatus includes a storage, a structuring circuitry, an identificationcircuitry, and an analysis circuitry. The storage is configured to storedata of a time-series medical image of a blood vessel of a subject. Thestructuring circuitry is configured to temporarily structure a dynamicalmodel of analysis processing based on the time-series medical image. Theidentification circuitry is configured to identify a latent variable ofthe dynamical model so that at least one of a prediction value of ablood vessel morphology and a prediction value of a bloodstream based onthe temporarily structured dynamical model is in conformity with atleast one of an observation value of the blood vessel morphology and anobservation value of the bloodstream measured in advance. The analysiscircuitry is configured to analyze the dynamical model to which theidentified latent variable is allocated.

A blood vessel analysis apparatus, a medical image diagnosis apparatus,and a blood vessel analysis method according to the present embodimentwill be hereinafter explained with reference to drawings.

A blood vessel analysis apparatus according to the present embodiment isa computer apparatus for performing structural fluid analysis on a bloodvessel region included in a medical image generated by a medical imagediagnosis apparatus. The blood vessel analysis apparatus according tothe present embodiment may be incorporated into a medical imagediagnosis apparatus, or may be a computer apparatus such as a workstation provided separately from the medical image diagnosis apparatus.In order to explain in a specific manner, the blood vessel analysisapparatus according to the present embodiment is considered to beincorporated into the medical image diagnosis apparatus in theexplanation below.

The medical image diagnosis apparatus according to the presentembodiment can be applied to any type of image diagnosis apparatusprovided with an imaging mechanism scanning a subject. For example, anX-ray computed tomography apparatus (X-ray CT apparatus), a magneticresonance diagnosis apparatus, ultrasonic diagnosis apparatus, SPECTapparatus, PET apparatus, radiological treatment apparatus, and the likecan be used as necessary as the medical image diagnosis apparatusaccording to the present embodiment. In order to explain in a specificmanner, the medical image diagnosis apparatus according to the presentembodiment is considered to be an X-ray computed tomography apparatus sin the explanation below.

FIG. 1 is a schematic block configuration diagram illustrating themedical image diagnosis apparatus according to the present embodiment(X-ray computed tomography apparatus). As shown in FIG. 1, the X-raycomputed tomography apparatus includes a CT gantry 10 and a console 20.The CT gantry 10 scans an imaging part of a subject using an X-ray inaccordance with the control of the gantry control circuitry 23 of theconsole 20. The imaging part is, for example, a heart. The CT gantry 10includes an X-ray tube 11, an X-ray detector 13, and a data acquisitioncircuitry 15. The X-ray tube 11 and the X-ray detector 13 are providedon the CT gantry 10 so as to be able to rotate about a rotation axis.The X-ray tube 11 emits an X-ray onto a subject in which a contrastagent is injected. The X-ray detector 13 detects the X-ray generatedfrom the X-ray tube 11 and transmitted through the subject, andgenerates an electric signal in accordance with the intensity of thedetected X-ray. The data acquisition circuitry 15 reads the electricsignal from the X-ray detector 13 and converts the electric signal intodigital data. A set of digital data for each view is referred to as araw data set. A time-series raw data set of multiple scan times aretransmitted by a non-contact data transmission apparatus (not shown) tothe console 20.

The console 20 has a system control circuitry 21 as a center, andincludes a gantry control circuitry 23, a reconstruction circuitry 25,an image processing circuitry 27, an input device 29, a display 31, anda storage 33. The console 20 is connected to an electrocardiograph 35.The electrocardiograph 35 generates cardiac beat information about thesubject, and provides the cardiac beat information to the gantry controlcircuitry 23 of the console 20.

The gantry control circuitry 23 controls each apparatus in the console20 in accordance with a scan condition set with the input device 29 bythe user. The gantry control circuitry 23 executes scan insynchronization with the cardiac beat information provided by theelectrocardiograph 35.

The reconstruction circuitry 25 generates data of a CT image of asubject based on a raw data set. More specifically, first, thereconstruction circuitry 25 generates a projection data set bypreprocessing the raw data set. The preprocessing includes logarithmictransformation, non-uniform correction, calibration correction, and thelike. Subsequently, the reconstruction circuitry 25 generates data of aCT image by applying image reconstruction processing to the projectiondata set. Existing algorithms such as analytic image reconstructionmethods such as filtered back projection (FBP) method and successiveapproximation image reconstruction such as maximum likelihoodexpectation maximization (ML-EM) method and ordered subset expectationmaximization (OS-EM) method can be applied as the image reconstructionalgorithm. In the present embodiment, the reconstruction circuitry 25generates time-series data of CT images based on time-series projectiondata set. The CT image includes pixel regions of blood vessels imagedwith the contrast agent (hereinafter referred to as blood vesselregions). It should be noted that the CT image may be slice datarepresenting two-dimensional space distribution of CT values, or may bevolume data representing three-dimensional space distribution of CTvalues. Hereinafter, a CT image is considered to be volume data. Thedata of the time-series CT image are stored in the storage 33.

The image processing circuitry 27 executes structural fluid analysis bystructuring a dynamical model based on the time-series CT images. Thedetails of the processing of the image processing circuitry 27 will beexplained later.

The input device 29 receives various kinds of commands and informationinputs from the user. A keyboard, a mouse, a switch, and the like can beused as the input device 29.

The display 31 displays various kinds of information such as a CT image,a structural fluid analysis result, and the like. For example, a CRTdisplay, a liquid crystal display, an organic EL display, a plasmadisplay and the like can be used as the display 31 as necessary.

The storage 33 is constituted by various kinds of storage media such asa hard disk apparatus. The storage 33 stores various kinds of data suchas time-series projection data, time-series CT image data, and the like.For example, the storage 33 stores time-series CT image data in amedical image file format based on digital imaging and communications inmedicine (DICOM) specification. The storage 33 may store medical datacollected by an external device in association with time-series CT imagedata in a medical image file.

The system control circuitry 21 includes a central processing unit(CPU), region only memory (ROM), a random access memory (RAM). Thesystem control circuitry 21 functions as the center of the X-raycomputed tomography apparatus. The system control circuitry 21 executesthe blood vessel structure analysis processing according to the presentembodiment by executing the blood vessel analysis program stored in theROM and the RAM.

It should be noted that the system control circuitry 21, the imageprocessing circuitry 27, the input device 29, the display 31, and thestorage 33 constitute the blood vessel analysis apparatus 50. Like thepresent embodiment, the blood vessel analysis apparatus 50 may beincorporated into the medical image diagnosis apparatus (X-ray computedtomography apparatus), or may be a computer apparatus providedseparately from the medical image diagnosis apparatus. The blood vesselanalysis apparatus 50 is provided separately from the medical imagediagnosis apparatus, the blood vessel analysis apparatus 50 may collectmedical data such as time-series CT images via a network from themedical image diagnosis apparatus and a picture archiving andcommunication systems (PACS).

Subsequently, an example of operation of the present embodiment will beexplained in details. The blood vessel analysis apparatus, medical imagediagnosis apparatus, blood vessel analysis method, and the blood vesselanalysis program according to the present embodiment can adopt, as theanalysis target, blood vessels in any portion of the human body such asa heart blood vessel, a carotid artery, and a cerebral artery. However,in order to explain in a specific manner, analysis target according tothe present embodiment is considered to be a blood vessel in the heartin the explanation below.

Examples of blood vessels of the heart include a coronary artery and anaorta. The coronary artery starts from the coronary artery origin of theaorta, runs on the cardiac muscle surface and enter into the endocardiumside from the epicardium side. The coronary artery branches intonumerous number of capillaries at the endocardium of the cardiac muscle.After the coronary artery branches into numerous number of capillaries,the numerous number of capillaries are united again to form a greatcardiac vein and connected to the coronary sinus. Unlike other organs,the coronary vascular system is characterized in that the perfusion isto be ensured in dynamics change of contraction and relaxation of thecardiac muscle.

The blood vessel analysis apparatus 50 according to the presentembodiment structures the dynamical model based on the time-series CTimages, and executes the structural fluid analysis on the blood vesselof the heart by using the dynamical model, and accurately calculates thedynamics index and the blood flow volume index in the blood vessel.Hereinafter, the blood flow volume index will be referred to as abloodstream index. In order to accurately calculate the dynamics indexand the bloodstream index, it is necessary to allocate a highly accuratelatent variable to the dynamical model. When the blood vessel analysisapparatus 50 structures the dynamical model, the blood vessel analysisapparatus 50 statistically identifies a latent variable by performinginverse analysis on the initial dynamical model. Therefore, the bloodvessel analysis apparatus 50 can accurately determine the latentvariable. The dynamics index means the dynamics index about the bloodvessel wall. The dynamics index about the blood vessel wall isclassified into, for example, an index of displacement of a blood vesselwall, an index of stress and distortion applied to a blood vessel wall,an index of internal pressure distribution applied to an intravascularlumen, an index of material characteristics representing the hardness ofthe blood vessel, and the like. The index of material characteristicsrepresenting the hardness of the blood vessel includes, e.g., an averageinclination of a curved line representing a relationship of stress anddistortion of a blood vessel tissue. The bloodstream index means anindex of hemodynamics about blood flowing in a blood vessel. Examples ofbloodstream indexes include the volume of flow of blood, the flow rateof blood, viscosity of blood, and the like.

The latent variable includes, for example, at least one of a parameterof a material model such as a material constitutive equation of bloodvessel or a material constitutive equation of blood (for example,Young's modulus, Poisson's ratio, and the like), a load conditionparameter such as an internal pressure distribution applied to anintravascular lumen, a boundary condition parameter of structureanalysis and fluid analysis, and a variation distribution parameterrelated to uncertainty of a time-series morphology index and shapedeformation index. In this case, a variation distribution parameterrelated to uncertainty of time-series morphology index and shapedeformation index is such that various kinds of uncertainties areexpressed as probability distributions in view of the fact that medicalimage data include variation distribution caused by noise of each CTvalue, probability distribution caused by ambiguity of a boundarythreshold value of a living tissue, and the like. Examples of variouskinds of uncertainties include uncertainty in a space coordinate ofboundary coordinates of blood vessel tissue and blood and feature points(such as a blood vessel branching portion, a contrast agent distributionarrangement, and the like), uncertainty of a geometric structureparameter (lumen radius and the like in a cross section perpendicular toa center line), and uncertainty of a medical image data itself (such asa CT value, a boundary threshold value, and the like).

The dynamical model is a numerical model expressing behavior of a bloodvessel and blood. The dynamical model has different types according toschemes of structural fluid analysis. For example, the dynamical modelis classified into continuum dynamical model and simplified dynamicalmodel. The continuum dynamical model is used for, for example, finiteelement method (FEM) and boundary element method. The simplifieddynamical model is classified into, for example, a material dynamicalmodel based on material dynamics and a fluid dynamical model based onflow studies. Unless otherwise specified in the following explanation,the type of the dynamical model is not particularly limited. The initialdynamical model is considered to mean a dynamical model allocated with asampling set about parameters of latent variables that can be obtainedfrom a variable range and probability distribution of latent variables(a set of combination of parameters).

FIG. 2 is a figure illustrating an example of a dynamical model M1 of atarget region of structural fluid analysis (hereinafter referred to asan analysis target region). As shown in FIG. 2, the dynamical model M1includes an aorta region R1, a right coronary artery region R2, and aleft coronary artery region R3. The blood flows from the aorta to theright coronary artery or the left coronary artery.

As shown in FIG. 2, in the dynamical model M1, the end at the side ofthe origin of the aorta is set as the entrance of the bloodstream, andthe end of the right coronary artery region and the end of the leftcoronary artery region are set as the exit of the bloodstream. Aboundary condition is set at each of the entrance and the exit. Theboundary condition about the entrance includes, for example, the flowrate of the bloodstream, or the pressure generated by the bloodstream atthe entrance, or the rate of change thereof. The boundary conditionabout the exit includes, for example, the flow rate of the bloodstream,or the pressure generated by the bloodstream at the exit, or the rate ofchange thereof. The deformation of the aorta, the right coronary artery,and the left coronary artery depends on various factors such asmechanical action to the blood vessel wall caused by the bloodstream,mechanical action to the blood vessel wall caused by the pulsation ofthe heart (external force), the load condition of the blood vessel crosssection boundary, the material model of the blood vessel wall,non-stress state of the blood vessel, geometric shape of the bloodvessel wall, and the like. In this case, the mechanical action to theblood vessel wall caused by the bloodstream includes, for example, theinternal pressure caused by the bloodstream and the shear stress causedby the bloodstream. Due to the internal pressure caused by thebloodstream, deformation occurs in the blood vessel circle or thedirection perpendicular to the intravascular lumen surface. With themechanical action to the blood vessel wall caused by the pulsation ofthe heart and the shear stress caused by the bloodstream, this generatesdeformation caused by the mechanical action applied to the blood vesselsuch as expansion and contraction, twisting, bending, and the like inthe blood vessel center line direction. The deformation of the bloodvessel such as expansion and contraction, twisting, bending, and thelike in the blood vessel center line direction is allocated, as the loadcondition, to the aorta region R1, the right coronary artery region R2,and the left coronary artery region R3. More specifically, thedeformation of the blood vessel such as expansion and contraction,twisting, bending, and the like in the blood vessel center linedirection is expressed by a forced displacement (movement vector androtation displacement) or a temporal change of a load vector. Thedeformation in the blood vessel circle or the direction perpendicular tothe lumen surface based on the internal pressure caused by thebloodstream is allocated to the intravascular lumen as a temporal changeof the pressure distribution.

The displacement constraint condition due to forced displacement isallocated to the aorta region R1, the right coronary artery region R2,and the left coronary artery region R3 in the structural fluid analysis.Therefore, the deformation freedom degree of the blood vessel wall inthe structural fluid analysis can be reduced, and the calculationconvergence can be stabilized, and the analysis time can be reduced.

For example, the deformation degree of the shape of the blood vesseldepends on the material of the blood vessel wall. For this reason, thematerial model is allocated to the aorta region R1, the right coronaryartery region R2, and the left coronary artery region R3. Thedeformation degree of the shape of the blood vessel also depends on thenon-stress state of the blood vessel. The residual stress distributionof the blood vessel may be allocated as the initial value of the loadcondition.

The parameters about latent variables such as the material model, theboundary condition, and the load condition are identified by the inverseanalysis (statistical identification processing) based on the dynamicalmodel explained later. The accurate latent variables identified by theinverse analysis region allocated to the dynamical model. With thedynamical model to which accurate latent variables region allocated, thehemodynamics analysis can be executed based on structural fluid analysisor fluid analysis or structure analysis or image analysis in view of theeffect to the analysis target blood vessel region due to the externalfactors such as the blood vessel and the heart outside of the analysistarget blood vessel region. When the blood vessel analysis apparatus 50structures the dynamical model, the blood vessel analysis apparatus 50can solve the following four difficulties associated with a conventionalexample using the identification of the latent variables with theinverse analysis. Difficulty 1: identification method of the materialmodel of the coronary artery. Difficulty 2: incorporation of the effectof deformation of the shape of the heart to the coronary artery.Difficulty 3: identification method of the boundary condition of thecoronary artery. Difficulty 4: image analysis and structural fluidanalysis using the blood vessel shape having variation based onuncertainty of medical image data. By overcoming the four difficulties,the blood vessel analysis apparatus 50 achieves the improvement of theanalysis accuracy as compared with a conventional blood vesselstructural fluid analysis in which latent variables are not identifiedwith the inverse analysis.

Subsequently, the details of the structural fluid analysis processingaccording to the present embodiment will be explained. FIG. 3 is afigure illustrating a typical flow of structural fluid analysisprocessing performed under the control of the system control circuitry21 according to the present embodiment. FIG. 4 is a figure illustratinga block configuration of the image processing circuitry 27.

As shown in FIG. 3, in the structural fluid analysis processing, first,the system control circuitry 21 reads a medical image file of processingtarget from the storage 33, and provides the medical image file to theimage processing circuitry 27. The medical image file includes not onlydata of time-series CT images but also data of observation value of thebloodstream index. The data of the time-series CT images are datarepresenting three-dimensional space distribution of time-series CTvalues. The time-series CT images include, for example, 20 CT images percardiac beat, and more specifically, the time-series CT images include,for example, CT images for about 20 cardiac phases.

As shown in FIG. 3, the system control circuitry 21 causes the imageprocessing circuitry 27 to perform the region setting processing (stepS1). In step S1, the region setting circuitry 51 of the image processingcircuitry 27 sets the analysis target region of the structural fluidanalysis in the blood vessel region included in the time-series CTimage, and sets the identification target region of the latent variablein the analysis target region. The analysis target region may be all ofthe blood vessel region drawn in the time-series CT image, or may be apart thereof. For example, the analysis target region is set to anygiven part of the blood vessel region with regard to the coronaryartery.

In this case, a structure of a blood vessel drawn in a CT image will beexplained with reference to FIG. 5. FIG. 5 is a figure schematicallyillustrating a cross section perpendicular to the center line of theblood vessel (hereinafter referred to as a center line longitudinalcross section). As shown in FIG. 5, the blood vessel includes atube-like blood vessel wall. The pixel region corresponding to the bloodvessel wall will be referred to as a blood vessel wall region. Thecenter axis of the blood vessel wall will be referred to as an axialline. The inner side of the blood vessel wall will be referred to as anintravascular lumen. A pixel region corresponding to blood where bloodand contrast agent flows in the lumen will be referred to as a bloodregion, and a pixel region corresponding to the contrast agent will bereferred to as a contrast agent region. When the blood region and thecontrast agent region are not particularly distinguished from eachother, it will be referred to as a lumen region. The border between thelumen and the blood vessel wall is referred to as a blood vessel wall.Outside of the blood vessel wall, perivascular tissues such as cardiacmuscles are distributed. The border between the blood vessel wall andthe perivascular tissue is referred to as a blood vessel external wall.Inside of the blood vessel wall, a plaque may be generated. A pixelregion corresponding to the plaque will be referred to as a plaqueregion. The plaque is classified into, for example, calcified plaquewhich has been calcified, atherosclerotic plaque, and the like. Theatherosclerotic plaque is soft, and the blood vessel wall may break andexude to the inside of the blood vessel as a thrombus, and may bereferred to as unstable plaque. Therefore, it is useful to find theproperty of the plaque in a clinical manner. As explained later, theproperty and the existing region of the plaque may be identified by a CTvalue.

For example, the analysis target region is set by the region settingcircuitry 51 to be limited into a lesion region or a treatment targetaccording to a command given with the input device 29 by the user. Forexample, in a case where a lesion region such as a plaque and a stenosedportion is found by image diagnosis and the like in advance, thepathologic change portion may be set as the analysis target region.Alternatively, the analysis target region may be limited in accordancewith a clinical empirical rule associated with a lesion region. Forexample, a pathologic change generated in the heart blood vessel islikely to be generated in the blood vessel running on the surface of theheart. In general, the blood vessel running on the surface of the heartis thicker than the blood vessel entering into the inside of the heart.Therefore, the region setting circuitry 51 may set the analysis targetregion in the blood vessel region associated with the blood vesselrunning on the surface of the heart according to the image processing orthe command given with the input device 29 by the user. For example, theregion setting circuitry 51 may set the analysis target region to belimited into a blood vessel region where the diameter is equal to ormore than 2 mm. In other words, the region setting circuitry 51 excludesthe blood vessel region associated with the blood vessel in the insideof the heart from the analysis target region. As described above, theprocessing efficiency can be improved by decimating the calculationtarget in a spatial manner.

When step S1 is performed, the system control circuitry 21 causes theimage processing circuitry 27 to perform image analysis/trackingprocessing (step S2). In step S2, the image analysis/tracking processingunit 53 of the image processing circuitry 27 performs image processingon the time-series CT images, and calculates time-series blood vesselmorphology index and the time-series blood vessel shape deformationindex. More specifically, the image analysis/tracking processing unit 53performs the image analysis processing on the time-series CT image tocalculate the time-series blood vessel morphology index, and performstracking processing on the time-series CT image to calculate thetime-series blood vessel shape deformation index. The blood vesselmorphology index is an index representing a mode of a blood vesselregion. A specific example of a blood vessel morphology index will beexplained later.

Hereinafter, image analysis/tracking processing will be explained in aspecific manner. In the image analysis processing, the imageanalysis/tracking processing unit 53 extracts the blood vessel regionfrom each CT image, and identifies an intravascular lumen region, ablood vessel wall region, and a plaque region as shown in FIG. 5. Theimage analysis/tracking processing unit 53 identifies, as blood vesselmorphology indexes, three-dimensional coordinates of multiple pixelsincluded in contours of the intravascular lumen region, the blood vesselwall region, and the plaque region. A three-dimensional coordinate of apixel is used as a blood vessel morphology index. It should be notedthat pixels of a particular target of a three-dimensional coordinate maybe limited to pixels included in the contours of the intravascular lumenregion, the blood vessel wall region, and the plaque region in a surfaceperpendicular to the intravascular lumen surface or the blood vesselcenter line longitudinal cross section. It should be noted that theblood vessel morphology index may be not only the three-dimensionalcoordinate but also geometric indexes such as a direction vector of zerodegrees and a radius and a diameter of the intravascular lumen for everyangle in the blood vessel center line longitudinal cross section, anaverage region size and an average radius for all the angles in thecross section, an intravascular lumen capacity enclosed by multiplecross sections perpendicular to the center line direction, a bloodvessel wall capacity and a plaque capacity enclosed by multiple crosssections perpendicular to the lumen surface.

In the tracking processing, the image analysis/tracking processing unit53 sets multiple feature points such as feature points, feature shapes,representative points, and pixels, in the blood vessel wall region andthe contrast agent region, according to the image processing or thecommand given with the input device 29 by the user. For example, theimage analysis/tracking processing unit 53 sets multiple feature pointssuch as a blood vessel branching portion and an anatomical feature pointon a surface. The image analysis/tracking processing unit 53 appliesimage tracking processing to multiple feature points at each time (eachcardiac phase), and calculates displacement values of multiple featurepoints. The image analysis/tracking processing unit 53 calculates atemporal change of a displacement value for each of multiple nodes inthe dynamical model based on the calculated displacement value by theinterpolation processing. For example, the image analysis/trackingprocessing unit 53 defines the node on the blood vessel center line inthe dynamical model. The image analysis/tracking processing unit 53 maycalculate the time-series deformation values (time change of thedeformation value) of twisting, bending, and expansion and contractionin the center line direction of the blood vessel based on thetime-series displacement value of nodes in the dynamical model (timechange of the displacement value). As explained later, the blood vesselshape deformation indexes such as the displacement value and thedeformation value are used as forced displacement in the dynamicalmodel. Hereinafter the time-series blood vessel morphology index will bereferred to as a shape history, and the time-series blood vessel shapedeformation index will be referred to as a forced displacement history.

FIG. 6 is a figure illustrating a temporal change of a mode of a centerline of a blood vessel region included in a CT image. As shown in FIG.6, for example, the time-series medical images include 20 CT images percardiac beat. More specifically, CT images are considered to be obtainedwith an interval of 5% from the cardiac phases 0% to 95%. The centerline of the blood vessel region is extracted by the imageanalysis/tracking processing unit 53. As shown in FIG. 6, the mode ofthe center line changes in accordance with the elapse of the cardiacphase.

FIG. 7 is a figure illustrating an example of tracking processingbetween a time t and a time t+Δt. As shown in FIG. 7, the nodes of thedynamical model from P1 to P20 are set on the blood vessel center lines.Each of the nodes of the dynamical model from P1 to P20 on the bloodvessel center line is mechanically connected to other nodes of thedynamical model on the center line longitudinal cross section includingthe node. However, they are independent from the nodes of the dynamicalmodel of the blood. Based on the displacement values of the featurepoints of the blood vessel, the displacement values of the nodes of P1to P20 on the blood vessel center line are calculated by processing suchas interpolation, and the forced displacement is set for each node.

In order to explain the blood vessel shape deformation index and theblood vessel morphology index, a local blood vessel region RA defined bythe node P13 and the node P14 will be considered. At a time t, thedistance between the node P13 and the node P14 in the center linedirection is considered to be L, and the radius in the blood vesselregion is considered to be r. The image analysis/tracking processingunit 53 calculates forced displacement such as expansion andcontraction, twisting, and bending in the blood vessel center linedirection of the node P13 and the node P14, so that the forceddisplacement at the node P13 (the movement displacement in thethree-dimensional space and the rotation displacement in the center linedirection) and the forced displacement at the node P14 (the movementdisplacement in the three-dimensional space and the rotationdisplacement in the center line direction) are calculated.

As shown in FIGS. 6 and 7, the image analysis/tracking processing unit53 calculates the forced displacement at each node on the center line(the movement displacement in the three-dimensional space and therotation displacement rotating about the center line) based on thecoordinate and the movement vector of the feature point, and calculatesthe blood vessel shape deformation index. For example, the imageanalysis/tracking processing unit 53 calculates a time change of acoordinate difference of two adjacent nodes is calculated as theexpansion and contraction distance ΔL in the center line. With regard toeach node on the center line, the image analysis/tracking processingunit 53 calculates a time change of a distance between the node inquestion and another node on the blood vessel region cross sectionincluding the node in question (the node in the intravascular lumen orthe blood vessel wall or the plaque region) as the expansion andcontraction distance Δr in the radius direction. With regard to eachfeature point, the image analysis/tracking processing unit 53 calculatesa bending angle Δθ in the center line direction of the node in questionon the center line based on the coordinates and the movement vectors ofmultiple feature points in proximity to the feature point.

In the above example, the spatial resolution of the time-series CT imageof the analysis target is expected to be constant over the elapse of thetime. However, the analysis target according to the present embodimentis not limited thereto.

For example, the X-ray computed tomography apparatus according to thepresent embodiment may generate time-series CT images by executing CTscan so that the temporal resolution of a designation section is higherthan the temporal resolution of another section. It should be noted thatsetting the temporal resolution of a designation section to be higherthan the temporal resolution of another section is considered to includea case where the temporal resolution of the another section is set to belower than the temporal resolution of the designation section. A CTimage outside of the designation section where the temporal resolutionis reduced may be interpolated based on another CT image adjacent to theCT image in question in terms of time. For example, the designationsection is set to a designation section in which the temporal resolutionis to be set to be higher and which is designated with the input device29 by the user. For example, the designation section may be designatedin a time section where movement of the blood vessel is intense. In atypical case, the movement of the blood vessel is relatively slow duringexpansion and contraction, and the noise of the time-series CT image isrelatively low. Therefore, the designation section is preferably setbetween the expansion and the contraction. Alternatively, a time sectionwhere the motion is relatively slow such as expansion and contractionmay be set as the designation section. A CT image in this time sectionwhen the motion is intense is preferably interpolated based on a CTimage in another time. Therefore, this can reduce noises in the CTimages in the time section where the motion of the blood vessel and thelike is intense, and further, enhance the accuracy of the structuralfluid analysis. As described above, the calculation time associated withthe structural fluid analysis can be reduced by decimating the CT imagesin terms of time.

The method for decimating the CT images of the analysis target in termsof time is not limited to changing the temporal resolution of the CTscan along the elapse of the time. For example, a CT image used for thestructural fluid analysis may be individually selected from originaltime-series CT images reconfigured by the reconstruction circuitry 25.For example, the above designation section is preferably set in theoriginal time-series CT image. In this case, a CT image is preferablyselected in a denser manner in terms of time in the designation sectionthan in other sections.

When step S2 is performed, the system control circuitry 21 causes theimage processing circuitry 27 to perform the structuring processing(step S3). In step S3, the dynamical model structuring circuitry 55 ofthe image processing circuitry 27 temporarily structures the dynamicalmodel about the analysis target region based on the time-series CTimage. More specifically, the dynamical model structuring circuitry 55temporarily structures the dynamical model about the analysis targetregion based on the time-series CT image and the forced displacementhistory and the shape history calculated based on the time-series CTimage. The dynamical model is a numerical model about the analysistarget region for performing the structural fluid analysis.

Hereinafter, step S3 will be explained in details. First, the dynamicalmodel structuring circuitry 55 structures the shape model for solvingthe dynamical model (mathematical model) based on the CT image and theshape history. The shape model schematically expresses the geometricstructure of the blood vessel region at each cardiac phase. The shapemodel is classified into, for example, multiple discretized regions. Thevertex of each of the discretized regions is referred to as a node. Thedynamical model structuring circuitry 55 may structure the shape modelfor each cardiac phase based on the blood vessel region and the bloodvessel morphology index included in the CT image for each cardiac phase,and may structure the shape model for each cardiac phase based on theblood vessel region and the blood vessel morphology index included inthe CT image of a particular cardiac phase. For example, when it isassumed that there is no residual stress in the blood vesselcorresponding to the analysis target region in the initial load state, atime phase at which the blood vessel corresponding to the analysistarget region is most greatly contracted in the time phase of thenon-stress state is assumed to be a non-stress state.

FIG. 8 is a figure illustrating a cross section perpendicular to acenter line of a shape model. As shown in FIG. 8, the shape model has anintravascular lumen region and a blood vessel wall region which regionranged from the center line to the outside. When there is a plaque, aplaque region may be provided in the blood vessel wall region. When theeffect to the blood vessel by the perivascular tissue is taken intoconsideration, the dummy element of the perivascular tissue may beprovided outside of the blood vessel wall region.

When the shape model is structured, the dynamical model structuringcircuitry 55 sets the sampling value about the parameter of the latentvariable obtained from the variable range and the probabilitydistribution of each latent variable (for example, sampling from a setof combination of parameters based on Markov chain Monte Carlo methodand the like) to the dynamical model. For example, as shown in FIG. 2,the dynamical model structuring circuitry 55 sets the region of theidentification target of the boundary condition about the entrance(hereinafter referred to as a boundary condition identification region)at the end of the aorta region R1 at the side of the origin of theaorta, and sets the boundary condition identification region about theexit at the end of the right coronary artery region R2 and the end ofthe left coronary artery region R3. The dynamical model structuringcircuitry 55 allocates the sampling value about the parameter of theboundary condition obtained from the variable range and the probabilitydistribution of the boundary condition to each boundary conditionidentification region. The dynamical model structuring circuitry 55 alsosets the region of the identification target of the material model(hereinafter referred to as a material model identification region) andthe region of the identification target of the load condition(hereinafter referred to as a load condition identification region) inthe aorta region R1, the right coronary artery region R2, and the leftcoronary artery region R3. The dynamical model structuring circuitry 55allocates the sampling value about the parameter of the material modelobtained from the variable range and the probability distribution of thematerial model to each material model identification region, andallocates the sampling value about the parameter of the load conditionobtained from the variable range and the probability distribution of theload condition to each load condition identification region. In theblood vessel, even when the volume of flow is zero, it is said thatthere is a residual stress. For example, the dynamical model structuringcircuitry 55 may allocate the residual stress in a case where the volumeof flow is zero to the analysis target region as the initial value ofthe load condition. The dynamical model structuring circuitry 55 may setan region of identification target of the geometric structure(hereinafter referred to as a geometric structure identification region)to a portion where there is uncertainty in the geometric structure. Itshould be noted that the parameter of the geometric structure is avariation distribution parameter related to uncertainty of the geometricstructure, or a variation distribution parameter involved in the CTimage, and may be, e.g., a variation distribution of the boundarythreshold value of the living tissue and the variation distribution ofeach CT value. The dynamical model structuring circuitry 55 may set thematerial model in the plaque region, the details of which will beexplained later. The details of the material model will be explainedlater.

The initial value of the boundary condition according to the fluidanalysis may be calculated based on the time-series CT image. In the CTscan using the contrast agent, the X-ray computed tomography apparatusrepeatedly performs scan with a lose dose on a scan region whileinjecting the contrast agent into the subject, and monitors the contrastagent density of the blood vessel in the scan region. Then, the X-raycomputed tomography apparatus performs scan with a normal dose when thecontrast agent density attains the already-determined value. The scanfor monitoring of the contrast agent density is referred to as apre-scan. The dynamical model structuring circuitry 55 may calculate theinitial value of the boundary condition according to the fluid analysisbased on the time-series CT image collected by pre-scan (so-called prepimage). For example, the dynamical model structuring circuitry 55 setsan region of interest (ROI) in the time-series CT image according to thecommand given with the input device 29 by the user. The ROI ispreferably set in the boundary condition identification region such asan entrance of the analysis target blood vessel. The dynamical modelstructuring circuitry 55 calculates parameters such as an initial speedand a flow-in volume of bloodstream in the ROI based on time change of apixel region of the contrast agent included in the ROI (hereinafterreferred to as a contrast agent region). For example, the dynamicalmodel structuring circuitry 55 applies image tracking processing to thecontrast agent region, thus calculating the parameters such as theinitial speed and the flow-in volume of the bloodstream. The convergencetime of the parameter of the latent variable can be reduced bycalculating the initial value of the boundary condition based on thetime-series CT image.

In order to easily identify the material model parameter, the dynamicalmodel structuring circuitry 55 may determine the material modelparameter allocated to the material model identification region based onthe CT value of a pixel in the material model identification region. Asdescribed above, the CT value is used as a plaque index for estimatingthe property of the plaque. Hereinafter, the determination processing ofthe material model parameter based on the CT value will be explainedusing an example of blood vessel wall.

The CT value is an index making the degree of attenuation of the X-rayinto a number in a relative manner. Therefore, the CT value has adifferent value for a different living tissue, and in other words, theCT value is scaled as necessary so that the difference of the tissue canbe recognized. The storage 33 includes a table storing each of theproperties of multiple blood vessel walls in association with the CTvalue range and the material model parameter. Hereinafter, this tablewill be referred to as a CT value-material model table.

FIG. 9 is a figure illustrating an example of a CT value-material modeltable stored in the storage 33. As shown in FIG. 9, the CTvalue-material model table includes items, i.e., the property of theblood vessel wall, a CT value range, and a material model parameter.Examples of properties include normal, atherosclerotic plaque, andcalcified plaque. For each property, the empirically defined CT valuerange and the material model parameter regions associated with eachother. The material model parameter may be defined by any given numeral,or may be defined in a numerical range. First, the dynamical modelstructuring circuitry 55 determines the representing pixel value in theROI included in the CT image. The ROI is set in the identificationtarget region of the material model according to the image processing orthe command given with the input device 29 by the user. For example, therepresenting pixel value is set to a statistical value such as anaverage value, a median value, a modal value, a maximum value, and aminimum value of pixel values of multiple pixels included in the ROI.The dynamical model structuring circuitry 55 identifies the materialmodel parameter associated with the representing pixel value by applyingthe determined representing pixel value to the CT value-material modeltable. The dynamical model structuring circuitry 55 allocates theidentified material model parameter to the material model identificationregion on the dynamical model corresponding to the ROI. As describedabove, by initially designating the material model parameter based onthe CT value, the search range of the parameter in the inverse analysisperformed later can be stenosed. Therefore, reduction of the calculationtime can be realized.

When the shape model is structured, the dynamical model structuringcircuitry 55 allocates the time-series blood vessel shape deformationindex calculated in step S2 to the shape model, and more specifically,the dynamical model structuring circuitry 55 allocates the forceddisplacement history to the shape model. The shape model to which thelatent variable and the forced displacement history region allocatedwill be referred to as a dynamical model.

In FIG. 10, the shape model M2 illustrates a portion of the dynamicalmodel of the blood vessel and the blood, and FIG. 10 is a figure forexplaining allocation of the forced displacement history to the node inthe dynamical model. FIG. 10 illustrates a portion of the shape modelM2. However, although FIG. 10 shows a case where the center line islocated in the M2, the center line may also be located outside of theM2. As shown in FIG. 10, multiple nodes PN (PN1, PN2) are set in theshape model M2. The node on the center line is referred to as PN1, andthe nodes in the dynamical model indicating the blood vessel and theblood will be referred to as PN2. The shape model M2 is set for thedummy element surface, blood vessel external wall, blood vessel wall,plaque region surface, plaque region internal, or blood portion. Thedynamical model structuring circuitry 55 allocates the forceddisplacement to each node PN1 of the shape model M2, and morespecifically, allocates the blood vessel shape change index for eachtime.

More specifically, the dynamical model structuring circuitry 55 connectsthe node PN1 and the node PN1, which region adjacent to each other onthe center line, with a beam element (or rigid element) EB. Thedynamical model structuring circuitry 55 connects the node PN1 andanother node PN2 included in a longitudinal cross section passingthrough the node PN1 with a beam element EB. The dynamical modelstructuring circuitry 55 allocates the constraint condition about theshape displacement direction of each blood vessel shape deformationindex to the node PN1 and the beam element EB. The forced displacementallocated to the region where the internal pressure of the materialmodel and the intravascular lumen is identified includes the expansionand contraction of the blood vessel wall (or dummy element) surface inthe center line direction, the twisting of the blood vessel wall (ordummy element) surface, and the bending deformation of the blood vesselwall (or dummy element) surface. For example, the forced displacementallocated to the region where the internal pressure of the materialmodel and the intravascular lumen is not identified includes not onlythe forced displacement in the center line direction and the time-seriesexpansion and contraction (displacement) of the blood vessel wall in theradius direction. In a case where the internal pressure affectsdeformation outside of the center line longitudinal cross section, theforced displacement is not allocated to the region, and the forceddisplacement history is allocated to only the peripheral portion of theregion (for example, the surface node of the dummy element). Cases wherethe internal pressure contributes to deformation outside of the centerline longitudinal cross section includes a case where there is aprotrusion on the intravascular lumen and the blood vessel branchingportion and the like. The dynamical model structuring circuitry 55allocates the time-series blood vessel shape deformation index to thenode PN1 and the beam element EB as the forced displacement history. Inthis manner, the expansion and contraction deformation, the twistingdeformation, and the bending deformation about the entire blood vesselor the local portion thereof are expressed.

In FIG. 10, the forced displacement history is set for the center lineunit and the external wall unit of the shape model, but the settingportion of the forced displacement history is not limited thereto. Forexample, the forced displacement history may be set in the blood vesselwall region between the center line unit and the external wall unit.

The image processing circuitry 27 according to the present embodimentperforms inverse analysis using the mechanical model temporarilystructured in step S3, and statistically identifies the latent variablethat is set in the dynamical model. The statistical identificationprocessing is performed in step S6 explained later. Steps S4 and S5 areprovided to calculate the blood vessel morphology index and thebloodstream index used for the statistical identification processing.

When step 3 is performed, the system control circuitry 21 causes theimage processing circuitry 27 to perform the blood vessel stressanalysis processing (step S4). In step S4, the blood vessel stressanalysis circuitry 57 of the image processing circuitry 27 performs theblood vessel stress analysis on the dynamical model of the currentstage, and calculates prediction value of the time-series blood vesselmorphology index. The blood vessel morphology index may be any of bloodvessel morphology indexes explained above, and, for example, it ispreferable to use the cross section shape index of the lumen region inthe blood vessel center line direction and the cross section shape indexof the blood vessel wall. More specifically, the cross section shapeindex of the lumen region is any one of the coordinate value of theattention-given pixel in the lumen region and the geometric structureparameter in the lumen region (the radius of the lumen region, thediameter of the lumen region, and the like). More specifically, thecross section shape index in the blood vessel wall region is any one ofthe coordinate value of the attention-given pixel in the blood vesselwall region and the geometric structure parameter in the blood vesselwall region (the radius of the blood vessel wall region, the diameter ofthe wall region, and the like). It should be noted that the predictionvalue means the calculation value of the blood vessel morphology indexcalculated by performing the blood vessel stress analysis on thedynamical model.

When step 3 is performed, the system control circuitry 21 causes theimage processing circuitry 27 to perform the blood fluid analysisprocessing (step S5). In step S5, the blood fluid analysis circuitry 59of the image processing circuitry 27 performs the blood fluid analysisto the temporarily structured dynamical model to calculate theprediction value of the time-series blood flow volume index. The bloodflow volume index is the volume of blood flow or the flow rate. Thebloodstream index may be any one of the volume of blood flow or flowrate, and a spatial or temporal average value of the volume of bloodflow or flow rate. It should be noted that the prediction value meansthe calculation value of the blood fluid index calculated by performingthe blood fluid analysis on the dynamical model.

When steps S4 and S5 are performed, the system control circuitry 21causes the image processing circuitry 27 to perform the identificationprocessing (step S6). In step S6, the statistical identificationcircuitry 61 of the image processing circuitry 27 statisticallyidentifies the parameter of the latent variable of the mechanical modelso that at least one of the prediction value of the blood vesselmorphology index calculated in step S4 and the prediction value of thebloodstream index calculated in step S5 are in conformity with at leastone of the observation value of the blood vessel morphology index andthe observation value of the bloodstream index collected in advance.

As shown in FIG. 4, the statistical identification circuitry 61 includesa first statistical identification circuitry 61-1 and a secondstatistical identification circuitry 61-2. The first statisticalidentification circuitry 61-1 statistically identifies the parameter ofthe latent variable so that the prediction value of the blood vesselmorphology index is in conformity with the observation value of theblood vessel morphology index. The second statistical identificationcircuitry 61-2 statistically identifies the parameter of the latentvariable so that the prediction value of the bloodstream index is inconformity with the observation value of the bloodstream index.Hereinafter, the first statistical identification circuitry 61-1 and thesecond statistical identification circuitry 61-2 will be explained inorder.

More specifically, in step S6, the first statistical identificationcircuitry 61-1 sets the data distribution based on the prediction valueand the observation value of the blood vessel morphology indexcalculated in step S4. The data distribution indicates, for example, amultivariate normal distribution function about an error of theprediction value and the observation value of the blood vesselmorphology index. More specifically, the first statisticalidentification circuitry 61-1 calculates the normal distributionfunction value of the error between the prediction value and theobservation value for each node or each element in the dynamical model.The first statistical identification circuitry 61-1 sets a product ofeach calculated normal distribution function value as data distribution.The data distribution may be set individually for each cardiac phase, ormay be set collectively for multiple cardiac phases.

Subsequently, the first statistical identification circuitry 61-1allocates prior distribution (prior probability distribution) to thelatent variable of the dynamical model. More specifically, the priordistribution is allocated to each parameter related to material model,boundary condition, load condition, and uncertainty of the shapedeformation index and the time-series morphology index. For example, theprior distribution related to the pressure value is allocated to thepressure value related to the intravascular lumen which is one of theparameters of the load condition. The range of the value that can betaken by the pressure value (hereinafter referred to as expected range)can be limited empirically in advance. The first statisticalidentification circuitry 61-1 executes Monte Carlo simulation of theinternal pressure value with limitation within the expected range, sothat the probability distribution of the internal pressure value, i.e.,the prior distribution, is calculated for each discretized region. Forexample, the first statistical identification circuitry 61-1 may set, asthe prior distribution, a probability distribution mathematicallyexpressed by a multivariate normal distribution function indicating thatthe inclination of average pressure change in the center line directionis negative in a predetermined case. Examples of predetermined casesinclude a case where the pressure distribution in the center linedirection is smooth, a case where the pressure change over the elapse ofthe time is smooth, and a case where any backward flow of bloodstream isobserved. In accordance with the probability distribution expected bybeing limited into the expected range, first statistical identificationcircuitry 61-1 executes the Monte Carlo simulation about the parameterof the load condition, and can obtain the sampling value of the loadcondition for setting in the dynamical model (latent variable).Subsequently, the first statistical identification circuitry 61-1performs the statistical identification processing on the priordistribution and the data distribution for each latent variable, andcalculates the posterior distribution (posterior probabilitydistribution). The statistical identification processing includes, forexample, hierarchical Bayesian model and Markov chain model. Then, thefirst statistical identification circuitry 61-1 identifies the parameterof each latent variable from the statistical value such as the modalvalue and the average value of the posterior distribution for eachlatent variable. For example, in the above example, the posteriordistribution of the intravascular lumen pressure value is calculated,and the identification value of the intravascular lumen pressure valueis calculated from the posterior distribution.

FIG. 11 is a figure for explaining identification of the averageinternal pressure and the posterior distribution calculation about theload condition based on hierarchical Bayesian model and Markov chainMonte Carlo method (the average pressure in the blood vessel). In thegraph as shown in FIG. 11, the vertical axis is defined as the internalpressure value, and the horizontal axis is defined as the distance fromthe blood vessel origin in the blood vessel center line direction. Asshown in FIG. 11, The calcified plaque region is set in the materialmodel identification region, and in the atherosclerotic plaque region,the material model identification region is set. The blood vesselinternal pressure decreases along the blood vessel center line directionfrom the blood vessel origin. Multiple nodes are set along the bloodvessel center line. In the longitudinal cross section (node crosssection) including each node, the posterior distribution of the lumeninternal pressure is calculated, and the modal value of the posteriordistribution is identified.

For example, the blood vessel morphology index calculated in step S2 isused as the observation value of the blood vessel morphology index.

The processing performed by the second statistical identificationcircuitry 61-2 is the same as the processing performed by the firststatistical identification circuitry 61-1 only in that the index usedfor calculation of the data distribution is different. Morespecifically, first, the second statistical identification circuitry61-2 sets the data distribution based on the prediction value and theobservation value of the bloodstream index calculated in step S5.Subsequently, the second statistical identification circuitry 61-2allocates the prior distribution to the latent variable of the dynamicalmodel. For example, the prior distribution of the parameter of thematerial model about the blood vessel, the parameter of the materialmodel about the blood, and the parameter of the material model about theplaque region allocated. Examples of parameters of the material modelinclude material model parameters such as parameters of an elasticmodulus and viscosity of the constitutive equation of the blood. Theprobability distribution and the expected range of the parameter of thematerial model can be set empirically in advance. The second statisticalidentification circuitry 61-2 sets the probability distribution of theparameter of the material model for each discretized region, and morespecifically, the second statistical identification circuitry 61-2 setsthe prior distribution, and in accordance with the expected probabilitydistribution limited into the expected range, the Monte Carlo simulationof the parameter of the material model can be executed, and the samplingvalue of the material model parameter for setting in the dynamical model(latent variable) can be obtained. Subsequently, the second statisticalidentification circuitry 61-2 calculates the posterior distribution byperforming the statistical identification processing on the priordistribution and the data distribution for each latent variable, andidentifies the parameter of each latent variable from the statisticalvalue of the calculated posterior distribution. For example, in theabove example, the posterior distribution of the parameter of thematerial model is calculated, and the identification value of theparameter of the material model is calculated from the posteriordistribution.

FIG. 12 is a figure for explaining the identification of the materialmodel parameter and the posterior distribution calculation about thematerial model parameter based on hierarchical Bayesian model and Markovchain Monte Carlo method (the equivalent elastic modulus of the bloodvessel wall). As shown in FIG. 12, the blood vessel morphology is thesame as FIG. 11. The posterior distribution of the parameter of thematerial model of the blood vessel wall (for example, the equivalentelastic modulus) is calculated by limiting into the material modelidentification region, and the modal value of the posterior distributionis identified as the identification value. As shown in FIG. 12, theidentification value of the material model parameter of the equivalentelastic modulus has a different value according to the calcified plaqueregion, the atherosclerotic plaque region, and the normal region. Inother words, the material can be found by observing the identificationvalue of the material model parameter of the equivalent elastic modulus.

It should be noted that the, observation value of the bloodstream indexis assumed to be, for example, the volume of blood flow change flown tothe aorta, and the observation value of the blood vessel morphologyindex can be used as the capacity change value (CFA) of the leftventricle measured by the image processing from the time-series CTimage. The temporal change of the movement amount of the feature pointis calculated by the image tracking of the contrast agent after thecontrast agent is injected into the coronary artery, so that the flowrate and the volume of flow may be calculated. The density change amountof the contrast agent in the temporal particular region or the bloodvessel center line direction is obtained, and the flow rate and thevolume of flow may be calculated from the temporal rate of change of thedensity change and the value obtained by dividing the density change bythe distance interval distance in the center line direction of eachregion. In the case of MRI, the image tracking of proton is used, and ina case of ultrasonic echo, the volume of flow is calculated by contrastechocardiography and the like.

In each step S6, both of the statistical identification processing withthe first statistical identification circuitry 61-1 and the statisticalidentification processing with the second statistical identificationcircuitry 61-2 may not be performed. More specifically, in step S6, anyone of the statistical identification processing with the firststatistical identification circuitry 61-1 and the statisticalidentification processing with the second statistical identificationcircuitry 61-2 may be performed.

In the above example, the first statistical identification circuitry61-1 statistically identifies the parameter of the latent variable sothat the prediction value of the blood vessel morphology index is inconformity with the observation value of the blood vessel morphologyindex, and the second statistical identification circuitry 61-2statistically identifies the parameter of the latent variable so thatthe prediction value of the bloodstream index is in conformity with theobservation value of the bloodstream index. However, the statisticalidentification circuitry 61 may statistically identify the parameter ofthe latent variable based on the structure-fluid interaction analysis sothat the prediction value of the blood vessel morphology index and theprediction value of the bloodstream index are in conformity with theobservation value of the blood vessel morphology index and theobservation value of the bloodstream index.

When step S6 is performed, the system control circuitry 21 causes theimage processing circuitry 27 to perform setting processing (step S7).In step S7, the dynamical model structuring circuitry 55 of the imageprocessing circuitry 27 sets the parameter of the latent variablecalculated in step S6 to the dynamical model.

When step S7 is performed, the system control circuitry 21 determineswhether the identification termination condition is satisfied or not(step S8). When the identification termination condition is determinednot to be satisfied in step S8 (step S8: NO), the system controlcircuitry 21 repeats steps S4, S5, S6, S7 and S8. In this case theidentification termination condition is expressed by whether the indexfor determining the identification termination (hereinafter referred toas an identification termination index) attains the defined value.Examples of identification termination indexes include a differencevalue between the prediction value and the observation value of theblood vessel morphology index. In this case, when this difference valueis more than an already-determined value, the system control circuitry21 determines that the identification termination condition is notsatisfied, and when the difference value is less than thealready-determined value, the system control circuitry 21 determinesthat the identification termination condition is satisfied. For example,the identification termination index may be the number of samplingpoints of the Monte Carlo method. In this case, when the number ofsampling points is less than the already-determined value, the systemcontrol circuitry 21 determines that the identification terminationcondition is not satisfied, and when the number of sampling points ismore than the already-determined value, the system control circuitry 21determines that the identification termination condition is satisfied.When the identification termination condition is determined to besatisfied, the dynamical model structuring circuitry 55 sets the latestdynamical model at that point in time to the ultimate dynamical model.

Steps S4, S5, S6, S7 and S8 explained above may be repeated according tothe same identification method, or may be repeated according todifferent identification methods. When steps S4, S5, S6, and S7 arerepeated in accordance with different identification methods, forexample, first, a simplified dynamical model may be used to temporarilyidentify the latent variable, and subsequently, a continuum dynamicalmodel may be used to accurately identify the latent variable. Asdescribed above, the statistical identification processing is performedin two steps in accordance with different schemes, and the parameter ofthe latent variable can be converged in a short time. A method using thesimplified dynamical model includes a method using an expression of amaterial dynamics of a thick cylinder and an expression ofHagen-Poiseuille flow and modified Bernoulli with the internal pressureand the external pressure. A method using a continuum dynamical modelincludes FEM structural fluid analysis.

When the identification termination condition is determined to besatisfied in step S8 (step S8: YES), the system control circuitry 21 maycause the image analysis/tracking processing unit 53 to perform amendingprocessing (step S9). In step S9, the image analysis/tracking processingunit 53 amends the shape of the blood vessel region included in thetime-series medical image so that the structural fluid analysis resultcarried out based on the latent variable obtained by the inverseanalysis according to the statistical identification method (theprediction values of the dynamical index and the prediction value of theblood fluid index) are in conformity with the observation values (theobservation value of the dynamical index and the observation value ofthe blood fluid index). The display 31 displays a diagnosis result basedon the amended time-series medical image. Therefore, the blood vesselanalysis apparatus 50 can display the diagnosis result in view of theultimate dynamical model. Alternatively, the display 31 may display, ona screen, a blood vessel portion/region in which the identification withthe inverse analysis and the observation result with the structuralfluid analysis are not in conformity.

When step S9 is performed, the system control circuitry 21 causes theimage processing circuitry 27 to perform the blood vessel stressanalysis processing (step S10). In step S10, the blood vessel stressanalysis circuitry 57 of the image processing circuitry 27 performs theblood vessel stress analysis on the ultimate dynamical model, andcalculates the space distribution of the prediction value of thetime-series dynamical index. More specifically, the prediction value ofthe dynamical index for each discretized region is calculated.

When step S9 is performed, the system control circuitry 21 causes theimage processing circuitry 27 to perform the blood fluid analysisprocessing (step S11). The blood fluid analysis circuitry 59 of theimage processing circuitry 27 in step S11 performs the blood fluidanalysis on the dynamical model temporarily structured, and calculatesthe space distribution of the prediction value of the time-seriesbloodstream index. More specifically, the prediction value of thebloodstream index for each discretized region is calculated.

It should be noted that the FFR may be calculated as the dynamical indexor the bloodstream index.

When steps S10 and S11 are performed, the system control circuitry 21causes the display 31 to perform the display processing (step S12). Instep S12, the display 31 displays the prediction value of thetime-series dynamical index calculated in step S10 and the predictionvalue of the time-series bloodstream calculated in step S11. Forexample, the display 31 displays the time-series dynamical index or thetime-series bloodstream index in a motion picture manner in which thetime-series dynamical model is in a color according to the predictionvalue. Therefore, the display 31 holds a color table indicating arelationship between various kinds of prediction values and color values(for example, RGB). The display 31 uses a color table to identify thecolor value according to the prediction value, and displays thediscretized region corresponding to the prediction value in a coloraccording to the color value identified.

FIG. 13 is a figure illustrating an example of display of a spacedistribution of an internal pressure which is one of dynamical indexes.As shown in FIG. 13, the display 31 displays each discretized regionconstituting the dynamical model in a motion picture manner in a coloraccording to the internal pressure value about the discretized region.When the user observes the dynamical model, the user can find, based onthe color, the dynamical index changing over time or space.

When step S11 is performed, the structural fluid analysis processing isterminated.

It should be noted that the amending processing in step S9 may not benecessarily performed. When the amending processing is not necessary,step S10 or step S11 may be performed when the identificationtermination condition is satisfied in step S8.

In the above embodiments, the allocation target of the constraintcondition of the forced displacement history is considered to beallocated uniformly to all the nodes. However, the present embodiment isnot limited thereto. For example, the allocation target of theconstraint condition of the forced displacement history may be dividedaccording to whether the boundary condition and the material model areidentified or not. FIGS. 14A and 14B are figures illustrating anotherexample of allocation of forced displacement history, and illustrates across section of the shape model. For example, when the boundarycondition and the material model are identified as shown in FIG. 14A,the forced displacement history is allocated only to the node PN2 on theexternal wall unit OW of the shape model, and the forced displacementhistory is allocated to the node PN3 of the blood vessel wall region RV.When the boundary condition and the material model are identified asshown in FIG. 14B, the forced displacement history is preferablyallocated to both of the node PN2 of the external wall unit of the shapemodel and the node PN3 of the blood vessel wall region RV. In this case,the forced displacement history is allocated to the node PN1 on thecenter line. The node PN1 and the node PN2 of the external wall unit OWmay be connected with the beam element EB, and the forced displacementhistory may be allocated to the nodes PN2 and PN3 on the beam elementEB. At this occasion, the contraction and the expansion in the circleare expressed as the expansion and contraction displacement of the beamelement EB. The forced displacement history may not be allocated to thelumen region RI in order to ensure the displacement freedom degree.

FIG. 15 is a figure illustrating another example of allocation of forceddisplacement history, and illustrates a cross section of the shape modelincluding the dummy element RD of the perivascular tissue. As shown inFIG. 15, the dummy element RD is set outside of the blood vessel wallregion RV. When the shape model includes the dummy element RD, the nodePN4 is set to not only the blood vessel wall region RV but also thedummy element RD. The forced displacement history is also allocated tothe node PN4. When the boundary condition and the material model areidentified, the dynamical model structuring circuitry 55 allocates theforced displacement history to the node PN 3 included in the bloodvessel wall region RV, and when the boundary condition and the materialmodel are not identified, the dynamical model structuring circuitry 55may not allocate the forced displacement history. When the forceddisplacement history is allocated to the node PN3, the material model isidentified in view of not only the shape index of the lumen region RIbut also the shape index of the blood vessel wall region RV.

As described above, the deformation freedom degree of the structuralfluid analysis can be suppressed by limiting the allocation target ofthe constraint condition, and the analysis can be performed in a stableand efficient manner. In a case where a dummy element is provided, andthe load with the internal pressure affects deformation outside of thecenter line longitudinal cross section, the load vector applied to theblood vessel and the internal pressure can be separated and the latentvariable can be identified in view of the morphology indexes of not onlythe intravascular lumen but also the blood vessel wall. Cases where theload given by the internal pressure affects deformation outside of thecenter line longitudinal cross section includes a case where there is aprotrusion on the intravascular lumen and the blood vessel branchingportion and the like. A dummy element set simulates a fat layer of awall surface in terms of physiology. On the other hand, in the numericalcalculation, a forced displacement is given to the blood vessel wallsurface with the dummy element set, so that there is an effect ofavoiding generation of a stress different from the reality in a localportion in the blood vessel wall.

FIG. 16 is a figure illustrating another example of allocation of forceddisplacement history, and illustrates a cross section of the shape modelincluding the plaque region RP. As shown in FIG. 16, the plaque regionRP is included in the blood vessel wall region RV. The plaque region RPis set in the material model identification region. For the plaqueregion RP, the material model is identified in view of the lumen shapeindex, the blood vessel wall shape index, and the plaque index (CTvalue). The dynamical model structuring circuitry 55 divides the plaqueregion into multiple partial regions in accordance with the property.For example, the dynamical model structuring circuitry 55 sets multiplelocal regions in the plaque region, and identifies the material modelparameter of each local region by using the CT value-material modeltable from the CT values of multiple pixels included in each localregion. Then, the dynamical model structuring circuitry 55 sets theidentified material model parameter (or, the parameter range) in thelocal region. Then, in each partial region, the parameter rangeaccording to the property of the partial region is preferably set inadvance. With the statistical identification processing explained above,the material model parameter for each partial region is identified bythe statistical identification circuitry 61. Then, in step S12, thedisplay 31 displays the index about the material characteristics of theblood vessel as the dynamical index, so that the user can accurately andeasily find the property of the plaque.

As shown in FIG. 16, the dynamical model structuring circuitry 55 mayconfigure such that the calculation density of the attention-givenregion such as a plaque region in the dynamical model is higher than thecalculation density of another region. It should be noted thatconfiguring the calculation density of the attention-given region to behigher than another region also includes configuring the calculationdensity of the another region to be lower than the calculation densityof the attention-given region. The calculation density can be adjustedaccording to the density of a lattice and a calculation element such asa node. It should be noted that the dynamical model structuringcircuitry 55 can set the attention-given region at any given location inaccordance with a command given with the input device 29 by the user.The calculation density of the attention-given region is configured tobe higher than another region, so that precise structural fluid analysiscan be performed by applying limitation into the attention-given regionwhile preventing the processing efficiency from being reduced. Inaddition, the calculation density in an region other than theattention-given region is configured to be lower than the calculationdensity of the attention-given region, so that the calculation speed canbe improved while the precision of the structural fluid analysis can bemaintained with regard to the attention-given region. It should be notedthat the attention-given region is not limited to the plaque region, andcan be set in any given region with the input device 29 by the user.

As described above, the blood vessel analysis apparatus 50 according tothe present embodiment includes the storage 33, the dynamical modelstructuring circuitry 55, the statistical identification circuitry 61,and the analysis circuitries 57, 59. The storage 33 stores data oftime-series medical images of blood vessels of a subject. The dynamicalmodel structuring circuitry 55 temporarily structures the dynamicalmodel of the analysis processing based on the time-series medicalimages. The statistical identification circuitry 61 identifies thelatent variable of the latent variable identification region so that atleast one of the prediction value of the blood vessel morphology indexand the prediction value of the bloodstream index based on thetemporarily structured dynamical model is in conformity with at leastone of the observation value of the blood vessel morphology index andthe observation value of the blood vessel morphology index measured inadvance. The analysis circuitries 57, 59 applies the analysis processingto the dynamical model to which the identified latent variable isallocated.

According to the above configuration, the blood vessel analysisapparatus 50 according to the present embodiment can identify the latentvariable such as material model, boundary condition, load condition, andgeometric structure, and the like by performing inverse analysis usingthe blood vessel shape deformation index and the bloodstream index. Theblood vessel analysis apparatus 50 repeatedly performs the inverseanalysis while changing the latent variable, so that the blood vesselanalysis apparatus 50 can identify the latent variable in view of all ofthe four difficulties explained above, i.e., 1. the identificationmethod of the material model of the coronary artery, 2. incorporation ofthe effect of the deformation of the shape of the heart to the coronaryartery, 3. the identification method of the boundary condition of thecoronary artery, and 4. the identification method of the load conditionand the boundary condition and the material model using the blood vesselshape having uncertainty. Therefore, the blood vessel analysis apparatus50 can execute the structural fluid analysis in view of the effect ofthe external factor such as the blood vessel, the heart, and the likethat are not drawn in a CT image.

In the CT scan, stress such as vasodilator drug is given to a subject inorder to enhance drawing performance and the like of blood vessel. Whenthe vasodilator drug is injected into the subject, the blood vessel ofthe subject expands, and the blood vessel region included in the CTimage becomes clear. However, the vasodilator drug causesarteriosclerosis in the blood vessel. In a case where the CT scan isexecuted under the stress, and time-series CT images (hereinafterreferred to as stress-applied CT image) are collected, parameters oflatent variables such as a boundary condition, a load condition, and thelike and the material model are identified by using the time-seriesstress-applied CT images. However, the parameter of the latent variablebased on the stress-applied CT image may be different from the parameterof the latent variable based on the heart blood vessel of the subject innormal circumstances (stress-less situation) in which no stress isapplied.

The blood vessel analysis apparatus 50 according to the presentembodiment makes the parameter of the latent variable to be closer tothe parameter of the stress-less situation, so that the time-series CTimage collected by the CT scan in the stress-less situation (hereinafterreferred to as a stress-less image) is used as the time-series CT imageof the analysis target. The blood vessel analysis apparatus 50 mayidentify parameters of latent variables such as a boundary condition, aload condition, and the like and the material model based on thetime-series stress-less image. In addition, The blood vessel analysisapparatus 50 makes the parameter of the latent variable based on thestress-applied CT image to be closer to the parameter of the stress-lesssituation, and therefore, correction can be done by using thestress-less image. As described above, the stress-applied CT image isused, so that the parameter of the latent variable can be made to becloser to the parameter in normal circumstances, and more accurate bloodvessel structure analysis can be performed.

As described above, according to the present embodiment, the precisionof the structural fluid analysis of the blood vessel can be improved.

Application Example

A blood vessel analysis apparatus 50 according to an application exampledetermines whether collateral exists or not by using the dynamical indexcalculated based on the dynamical model.

FIGS. 17A and 17B are figures for explaining collateral. FIG. 17A is aschematic diagram of a heart where collateral does not exist. FIG. 17Bis a schematic diagram of a heart where collateral exists. As shown inFIG. 17A, in a case where the blood vessel is closed due to stenosis,thrombus, and the like, blood does not flow in the blood vessel at thedownstream of the obstruction existing portion RS, and no blood spreadsto the dominating region of the blood vessel. Therefore, the tissue inthe dominating region is necrotized. In order to allow the blood tospread into the dominating region, a detour circuit to the dominatingregion is formed as shown in FIG. 17B. This detour circuit is amicrovessel called a collateral RC. With the collateral RC, blood streamin the blood vessel at the downstream of the obstruction portion to thedominating region is ensured, and the perfusion to the heart ismaintained. With the collateral, the necrosis region RN is reduced. Thecollateral is very small, and normally, the collateral is not drawn inthe CT image. Therefore, in a case where there is a collateral, thebehavior with the collateral cannot be completely reflected in thedynamical model based on the blood vessel region drawn in the CT image,and the accuracy of the dynamical model is degraded.

The image processing circuitry 27 according to the application exampledetermines whether there is collateral or not, and therefore, the imageprocessing circuitry 27 further includes a collateral determinationcircuitry 63 as shown in FIG. 4. The image processing circuitry 27according to the application example can restructure the dynamical modelin view of the existence of the collateral.

The details of processing according to the application example will behereinafter explained. FIG. 18 is a figure illustrating a typical flowof processing performed under the control of the system controlcircuitry 21 according to the application example.

For example, the system control circuitry 21 starts processing accordingto the application example as shown in FIG. 18 upon receiving adetermination start command for determining whether collateral exists ornot. For example, this determination start command is preferablyautomatically issued after step S9 of FIG. 3 (in a case where theidentification termination condition is determined to be satisfied instep S8 in a case where step S9 is not performed). The determinationstart command may be input with the input device 29 by the user anygiven point in time after the dynamical model is structured. In thiscase, it is considered that the storage 33 is storing a dynamical modelstructured by the processing of FIG. 3 explained above.

Upon receiving a determination start command, the system controlcircuitry 21 causes the image processing circuitry 27 to perform theblood fluid analysis processing (step S21). According to the same methodas step S10, the blood vessel stress analysis circuitry 57 of the imageprocessing circuitry 27 in step S21 performs the blood vessel stressanalysis on the dynamical model structuring circuitry, and calculatesthe space distribution of the prediction value of the dynamical index.In a case where the determination start command is input with the inputdevice 29 by the user, the blood vessel stress analysis circuitry 57reads the dynamical model from the storage 33, and calculates the spacedistribution of the prediction value of the dynamical index based on theread dynamical model. The index affected by whether the collateralexists or not is employed as the dynamical index calculated in step S21.More specifically, dynamical indexes calculated in step S21 include theinternal pressure, the stress, the distortion, and the like.

When step S21 is performed, the system control circuitry 21 causes theimage processing circuitry 27 to perform the determination processing(step S22). In step S22, the collateral determination circuitry 63 ofthe image processing circuitry 27 determines whether there is collateralor not based on the change mode of the prediction value of thetime-series dynamical index in the center line direction.

FIG. 19 is a figure for explaining determination processing in which thecollateral determination circuitry 63 determines whether there iscollateral or not. FIG. 19 illustrates a graph illustrating a spacedistribution of an internal pressure. The vertical axis of the graph ofFIG. 19 is defined as an internal pressure which is one of the dynamicalindexes, and the horizontal axis is defined as a blood vessel centerline direction distance from the blood vessel origin. As shown in FIG.19, in a case where there is not any obstruction due to stenosis,thrombus, and the like (dotted line of FIG. 19), the internal pressuregradually decreases as the distance from the origin increases. Whenobstruction exists, the internal pressure rapidly decreases at theobstruction existing portion. In a case where obstruction exists, and nocollateral exists (broken line in FIG. 19), the internal pressure thathas rapidly decreased at the obstruction existing portion is still at alow level even if the distance from the origin further increases.However, in a case where obstruction exists, and collateral exists(alternate long and short dashed line FIG. 19), the internal pressurethat has rapidly decreased in the obstruction existing portion increasesagain in the portion where the collateral exists. As described above, ina case where collateral exists, the space distribution of the internalpressure indicates a peculiar change mode. For example, in a case of FFRmeasurement using a catheter, an FFR value is measured based on theinternal pressure difference of two points. Therefore, in a case wherethe internal pressures at two points sandwiching the portion where thecollateral exists are measured, the FFR value based on the internalpressures of the two points cannot reflect the existence of thecollateral.

The collateral determination circuitry 63 determines whether collateralexists or not by using a peculiar change mode of the space distributionof the internal pressure value indicated in a case where collateralexists. More specifically, the collateral determination circuitry 63analyzes the shape in the change curved line of the internal pressurevalue along the distance from the origin, and after the internalpressure value once rapidly decreases as the distance from the originincreases, the collateral determination circuitry 63 determines whetherthe internal pressure value turns to increase or not. For example, thecollateral determination circuitry 63 calculates the distancedifferential value of the internal pressure value with a regulardistance, and in a case where the distance differential value of theinternal pressure value is equal to or less than a threshold value(hereinafter referred to as a decrease threshold value), the internalpressure value is determined to have rapidly decreased. In a case wherethe distance differential value of the internal pressure value isdetermined to be less than the decrease threshold value, the collateraldetermination circuitry 63 sets the position in the obstruction existingportion. The collateral determination circuitry 63 calculates thedistance differential value of the internal pressure value from theobstruction existing portion with a regular distance interval, and in acase where the distance differential value of the internal pressurevalue is equal to or more than a threshold value (hereinafter referredto as an increase threshold value), the internal pressure value isdetermined to have increased. In a case where the distance differentialvalue of the internal pressure value is determined to be more than theincrease threshold value, the collateral determination circuitry 63determines that the collateral exists. In a case where the distancedifferential value of the internal pressure value is determined to bemore than the increase threshold value, the collateral determinationcircuitry 63 sets the position in the portion where collateral exists.It should be noted that the decrease threshold value the increasethreshold value explained above can be set separately from any givenvalue. As described above, in a case where the internal pressure valueonce rapidly decreases, and turns to increase, the collateraldetermination circuitry 63 determines that collateral exists, and in acase where the internal pressure value indicates another change mode,the collateral determination circuitry 63 determines that no collateralexists. The internal pressure is explained as an example of a dynamicalindex in order to explain determination processing of collateralperformed by the collateral determination circuitry 63 in a morespecific manner, but the dynamical index that can be used for thedetermination processing of the collateral is not limited to theinternal pressure. For example, not only the internal pressure but alsostress, distortion, and the like can be used in the same manner for thedetermination processing of the collateral.

When step S22 is performed, the system control circuitry 21 causes thedisplay 31 to perform the display processing (step S23). In step S23,the display 31 displays the determination result indicating whethercollateral exists or not given by the collateral determination circuitry63 in step S22. More specifically, in a case where the collateraldetermination circuitry 63 determines that collateral exists, thedisplay 31 displays a message to that effect, and in a case where thecollateral determination circuitry 63 determines that no collateralexists, the display 31 displays a message to that effect. In a casewhere collateral is determined to exist, the display 31 may clearlyindicate a portion where the collateral exists on the graph of FIG. 19and the dynamical model. Further, the display 31 may clearly indicate anobstruction portion of stenosis, thrombus, and the like on the graph ofFIG. 19 and the dynamical model.

When step S23 is performed, the system control circuitry 21 determineswhether the system control circuitry 21 performs identification of thelatent variable again or not (step S24). For example, in a case wherethe collateral is determined to exist, the system control circuitry 21determines to automatically perform identification of the latentvariable again in step S24, and in a case where the collateral isdetermined not to exist, the system control circuitry 21 determines notto automatically perform identification of the latent variable again. Ina case where a command for performing the perform identification againis given with the input device 29 by the user, it may be determined toperform the identification of the latent variable again, and in a casewhere the command is not given with the input device 29, it may bedetermined not to perform the identification of the latent variableagain. In this case, the user refers to the determination result as towhether the collateral exists or not, and determines whether it isnecessary to perform the identification of the latent variable again ornot. For example, the user observes a CT image, and in a case where thecollateral is determined to be likely to exist, it is determined that itis necessary to perform the identification of the latent variable again.In a case where it is determined that it is necessary to perform theidentification of the latent variable again, the user inputs a commandfor performing the identification of the latent variable again with theinput device 29.

In a case where it is determined that the identification of the latentvariable is not performed again in step S24 (step S23: NO), the systemcontrol circuitry 21 terminates the processing according to theapplication example.

In a case where the identification of the latent variable is determinednot to be performed again in step S24 (step S23: YES), the systemcontrol circuitry 21 causes the image processing circuitry 27 to performthe setting processing (step S25). In step S25, the dynamical modelstructuring circuitry 55 of the image processing circuitry 27 sets, inthe dynamical model, a boundary condition in view of the existence ofthe collateral. For example, dynamical model structuring circuitry 55sets, in a portion where the collateral exists, the initial value of theboundary condition about the flow in and flow out of the blood with thecollateral. A value in a range empirically defined may be used asnecessary as the initial value of the flow in and flow out condition.

When the boundary condition in view of the existence of the collateralis set, the system control circuitry 21 repeatedly performs steps S4,S5, S6, S7, and S8, and determines the parameters of the latentvariables of the dynamical model in view of the existence of thecollateral. After the parameters of the latent variables are determinedin step S9, amending of the medical image analysis/image trackingprocessing and display of the amending result are performed, and thetime-series dynamical index is calculated based on the dynamical modelin step S10, and the time-series bloodstream index is calculated basedon the dynamical model in step S11, and the time-series dynamical indexand the time-series bloodstream index are displayed in step S12. Thesesteps S4, S5, S6, S7, S8, S9, S10, S11, and S12 are the same as theprocessing as shown in FIG. 3, and therefore they are not explainedrepeatedly.

The explanation about the processing according to the applicationexample has been hereinabove explained.

According to the application example, whether there is collateral thatis not drawn on the CT image can be determined by using the dynamicalmodel. Then, an accurate dynamical model in view of the existence of thecollateral can be structured by re-identifying the parameters of thelatent variables in view of the existence of the collateral by using theinverse analysis.

While certain embodiments have been described, these embodiments havebeen presented by way of example only, and are not intended to limit thescope of the inventions. Indeed, the novel embodiments described hereinmay be embodied in a variety of other forms; furthermore, variousomissions, substitutions and changes in the form of the embodimentsdescribed herein may be made without departing from the spirit of theinventions. The accompanying claims and their equivalents are intendedto cover such forms or modifications as would fall within the scope andspirit of the inventions.

1. A blood vessel analysis apparatus comprising: a storage configured tostore data of a time-series medical image of a blood vessel of asubject; a structuring circuitry configured to temporarily structure adynamical model of analysis processing based on the time-series medicalimage; an identification circuitry configured to identify a latentvariable of the dynamical model so that at least one of a predictionvalue of a blood vessel morphology and a prediction value of abloodstream based on the temporarily structured dynamical model is inconformity with at least one of an observation value of the blood vesselmorphology and an observation value of the bloodstream measured inadvance; and an analysis circuitry configured to analyze the dynamicalmodel to which the identified latent variable is allocated.
 2. Theapparatus according to claim 1, wherein the analysis circuitry performsstructure analysis, fluid analysis, or, structural fluid interactionanalysis with the dynamical model to which the identified latentvariable is allocated, and calculates at least one of a prediction valueof the time-series dynamical index and a prediction value of thetime-series bloodstream.
 3. The apparatus according to claim 1, whereinthe identification circuitry identifies the latent variable of thedynamical model so that the prediction value of the blood vesselmorphology and the, prediction value of the bloodstream are inconformity with the observation value of the blood vessel morphology andthe observation value of the bloodstream.
 4. The apparatus according toclaim 1 further comprising: a setting circuitry configured to set ananalysis target region in a blood vessel region included in thetime-series medical image, and set a latent variable identificationregion in the analysis target region; an image processing circuitryconfigured to calculate a time-series morphology index and a time-seriesshape deformation index of the analysis target region by performingimage processing of the time-series medical image, wherein thestructuring circuitry temporarily structures a dynamical model ofanalysis processing of the analysis target region, based on thetime-series morphology index, the time-series shape deformation index,and the time-series medical image.
 5. The apparatus according to claim4, wherein the setting circuitry locally sets the analysis target regionto a lesion region or a treatment target.
 6. The apparatus according toclaim 4, wherein the setting circuitry locally sets the analysis targetregion to a blood vessel region where a diameter is equal to or morethan 2 mm.
 7. The apparatus according to claim 4, wherein the settingcircuitry excludes a pixel region of a blood vessel in a heart from theanalysis target region.
 8. The apparatus according to claim 1 furthercomprising a determination circuitry and a display, wherein the analysiscircuitry calculates a space distribution of a time-series dynamicalindex, based on a dynamical model to which the identified latentvariable is allocated, the determination circuitry determines whethercollateral exists or not based on a change mode of the time-seriesdynamical index in a blood vessel center line direction, and the displaydisplays a determination result given by the determination circuitry. 9.The apparatus according to claim 1, further comprising a storage storingeach of a plurality of CT value ranges in association with a property ofa blood vessel wall and a material model, wherein the latent variableincludes a material model, the time-series medical image is atime-series CT image generated by an X-ray computed tomographyapparatus, and, the structuring circuitry identifies a material modelassociated in the storage with a CT value of a pixel constituting ablood vessel wall region included in the medical image, and allocatesthe identified material model to the blood vessel wall region of thedynamical model.
 10. The apparatus according to claim 1, wherein thelatent variable includes a boundary condition related to a blood flowinlet and a blood flow outlet, and the structuring circuitry calculatesan initial value of the boundary condition based on the time-series CTimage generated by a CT scan for a subject injected with a contrastagent, by an X-ray computed tomography apparatus.
 11. The apparatusaccording to claim 1, wherein the structuring circuitry sets a highercalculation density to an interest region in the dynamical model thananother region.
 12. The apparatus according to claim 1, wherein thetime-series medical image is a time-series CT image generated by anX-ray computed tomography apparatus, and the X-ray computed tomographyapparatus executes CT scan so that a temporal resolution of adesignation section in the time-series CT image is higher than atemporal resolution of another section.
 13. The apparatus according toclaim 1, wherein the time-series medical image is a time-series CT imagegenerated by an X-ray computed tomography apparatus when the subject isin a stress-less situation.
 14. A medical image diagnosis apparatuscomprising: an imaging mechanism configured to generate data of atime-series medical image of a blood vessel of a subject; a structuringcircuitry configured to temporarily structure a dynamical model ofanalysis processing based on the time-series medical image; anidentification circuitry configured to identify a latent variable of thedynamical model so that at least one of a prediction value of a bloodvessel morphology and a prediction value of a bloodstream based on thetemporarily structured dynamical model is in conformity with at leastone of an observation value of the blood vessel morphology and anobservation value of the bloodstream measured in advance; and ananalysis circuitry configured to analyze the dynamical model to whichthe identified latent variable is allocated.
 15. A blood vessel analysismethod comprising: temporarily structuring a dynamical model of analysisprocessing based on a time-series medical image of a blood vessel of asubject; identifying a latent variable of the dynamical model so that atleast one of a prediction value of a blood vessel morphology and aprediction value of a bloodstream based on the temporarily structureddynamical model is in conformity with at least one of an observationvalue of the blood vessel morphology and an observation value of thebloodstream measured in advance; and analysing the dynamical model towhich the identified latent variable is allocated.
 16. A blood vesselanalysis apparatus comprising: a storage configured to store data of atime-series medical image of a blood vessel of a subject; a calculationcircuitry configured to calculate a dynamical index of the blood vesselin a center line direction of the blood vessel based on the time-seriesmedical image; a determination circuitry configured to determine whethercollateral exists or not based on a change mode of a dynamical index ofthe dynamical index in the center line direction; and the displaydisplays a determination result given by the determination circuitry.17. The apparatus according to claim 16, wherein the calculationcircuitry calculates the time-series dynamical index in the center linedirection, based on the time-series medical image, and the determinationcircuitry determines whether collateral exists or not based on a changemode of the time-series dynamical index.